A Chinese Word Clustering Method Using Latent Dirichlet Allocation and K-means

DOI

10.2991/cse.2013.60How to use a DOI?

Keywords

word clustering; latent dirichlet allocation; k-means; word similarity

Abstract

Word clustering is a popular research issue in the field of natural language processing. In this paper, Latent Dirichlet Allocation algorithm is used to extract the topics from nouns in the text, and the highest probability noun of each topic is selected as the centroids of the k-means algorithm. Experimental results show that this method can get better effects than the graph-based word clustering algorithms using a web search engine.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Lin Qiu
AU  - Jungang Xu
PY  - 2013/07
DA  - 2013/07
TI  - A Chinese Word Clustering Method Using Latent Dirichlet Allocation and K-means
BT  - Proceedings of the 2nd International Conference on Advances in Computer Science and Engineering (CSE 2013)
PB  - Atlantis Press
SP  - 269
EP  - 272
SN  - 1951-6851
UR  - https://doi.org/10.2991/cse.2013.60
DO  - 10.2991/cse.2013.60
ID  - Qiu2013/07
ER  -